The impact of tertiary lymphoid structures on tumor prognosis and the immune microenvironment in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a common malignancy whose prognosis and treatment outcome are influenced by many factors. Some studies have found that tertiary lymphoid structures (TLSs) in cancer may contribute to prognosis and the prediction of immunotherapy efficacy However, the combined role of TLSs in NSCLC remains unclear. We accessed The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to obtain mRNA sequencing data and clinical information as the TCGA cohort, and used our own sample of 53 advanced NSCLC as a study cohort. The samples were divided into TLS+ and TLS- groups by pathological tissue sections. Patients of the TLS+ group had a better OS (p = 0.022), PFS (p = 0.042), and DSS (p = 0.004) in the TCGA cohort, and the results were confirmed by the study cohort (PFS, p = 0.012). Furthermore, our result showed that the count and size of TLSs are closely associated with the efficacy of immunotherapy. In addition, the TLS+ group was associated with better immune status and lower tumor mutation load. In the tumor microenvironment (TME), the expression levels of CD4+ T cells and CD8+ T cells of different phenotypes were associated with TLSs. Overall, TLSs are a strong predictor of survival and immunotherapeutic efficacy in advanced NSCLC, and T cell-rich TLSs suggest a more ordered and active immune response site, which aids in the decision-making and application of immunotherapy in the clinic.


Evaluation of TLSs in pathological sections
We evaluated the density of lymphocyte infiltration by HE pathologically stained sections of the TCGA cohort and study cohort.This study used methods based on this definition 14 for counting all forms of TLS as follows: (1) lymphocyte aggregates (Agg) with lymphocyte infiltration but no lymphoid follicle formation; (2) primary follicles (FL-I), with well-defined clusters of round or oval lymphocytes or plasma cells (no germinal centers present); and (3) secondary follicles (FL-II), with well-defined clusters of round or oval lymphocytes or plasma cells (germinal centers present).
In our study, TLSs+ is defined as at least 1 lymphocyte aggregation structure found in the HE section of the subject, and the opposite is defined as TLSs-.

Analysis of gene expression differences
To explore variances in gene expression between the two cohorts, we selected tumor tissue samples with both hematoxylin and eosin (H&E) staining slides and RNAseq expression data.These samples were then grouped based on the presence or absence of tertiary lymphoid structures (TLS), designated as TLS+ or TLS-, respectively.Differential expression analysis was performed using the R package "edgeR".Initially, samples with zero expression were excluded.Differential expression analysis was performed to explore differences between the two groups.Finally, gene set enrichment analysis (GSEA) was performed using the KEGG and Reactome databases to clarify the signaling pathways in the locations of the differential genes.

Immune cell infiltration analysis
Belonging to the immune structure family, TLSs are hypothesized to correlate with immune cell infiltration.Consequently, we computed the immune infiltration score for each patient's cancer tissue employing CIBER-SORT.Spearman correlation analysis was utilized to examine the association between signature genes and the immune infiltration score.Lastly, the impact of copy number variation (CNV) on immune cell infiltration was investigated through gene set cancer analysis (GSCA).

Immune microenvironment analysis
Immune cell infiltration was identified using timer 2.0 (cistrome.shinyapps.io/timer/)via the MCPCOUNTER, CIBERSORT, QUANTISEQ, Timer, CIBERSORT-ABS, EPIC, and XCELL algorithms.Infiltration levels of stromal and immune cells can be calculated with the ESTIMATE algorithm 15 .Concentration scores of 16 immune cells were calculated using "GSEABase" and "GSVA" packages.The TIMER database studied six immune cells (B cells, macrophages, neutrophils, dendritic cells, CD8+ T cells, and CD4+ T cells) infiltration for its association with TLSs expression.The expression of multiple immune checkpoint molecules was compared to determine whether there were differences in immune checkpoint blockade (ICB) therapy between the TLSs+ and TLSsgroups.Immune checkpoints with differential expression between the two groups were visualized.Additionally, TIDE (Tumor Immune Dysfunction and Exclusion) score was calculated online following the instructions (https:// tide.dfci.harva rd.edu/).An inverse correlation was found between the TIDE score and ICB treatment success 16 .

Antigen presentation analysis
Human leukocyte antigen (HLA), found on numerous immune cell surfaces, is crucial for triggering cellular and humoral immunity 17 .To determine whether or not there were distinctions in antigen expression between the two groups, the "limma" package was used to compare the HLA expression levels of the two groups.

Cancer stem cell infiltration analysis
We utilized the UCSC Xena browser (http:// xena.ucsc.edu/) to extract DNA methylation-based stemness scores (DNAss) and RNA-based stemness scores (RNAss) for TCGA-LUAD patients.A comparative analysis was then performed at both the DNA and RNA levels to investigate differences in stem cell infiltration between the two groups.

Predicting drug therapeutic response
The Cancer Immunome Atlas (https:// tcia.at/) was employed to derive the immunophenoscore (IPS) for predicting sensitivity to immunotherapy.Additionally, the IC50 values of common chemotherapeutic agents within the entire TCGA cohort were calculated using the "pRRophetic" software package to evaluate the predictive capacity of AGRs for drug treatment response.Subsequently, differences in IC50 values between the two groups were compared utilizing the Wilcoxon rank-sum test.Finally, the results were visualized through bar charts.

Multiplex IHC staining
Frozen sections and formalin-fixed paraffin-embedded (FFPE) tissue sections were used.Opal 7-color kit (Perki-nElmer) was used for multiplex IHC and is summarised in Extended Data Table 1.Four micrometers of FFPE sections were dewaxed and rehydrated.Human FFPE tonsil sections were used as positive controls for CD3, CD4, CD8, CD20, CD21, CD23, CK, Foxp3, TCF1, DC-LAMP, PD1, and PNAd, and lung cancer tissue was used as a negative control.CD3, CD20, CD21, CD23, DC-LAMP, and PNAd form panel 1 and mark the TLSs.The remaining antibodies formed panel 2 and labeled the TILs in the TME.Antigen retrieval using high pH (MXB) or low pH (Servicebio) antigen extracts in a microwave oven.Antibodies used for Multiplex IHC are summarised in Extended Data Table 2.For the 2 panels staining, a tyramide system amplification (TSA) was used.In the first round antigen was retrieved with a microwave oven at 100% fire for 150 s, and 30% fire for 12 min.Slides were cooled to room temperature (RT) and washed with TBST/0.5% Tween (3 times, 5 min).Slides were washed and blocked with blocking buffer (BLOCJING/AB DILUENT) for 12 min.The primary antibody was incubated at 37℃ for 1 h or 4℃ overnight.Slides were washed and an HRP-conjugated secondary antibody was incubated at 37℃ for 10 min.TSA dye (1:100) was applied for 10 min at 37℃ after washes.This was repeated five more times using the remaining antibodies.Nuclei were stained with DAPI (PerkinElmer) and mounted with a coverslip.Secondary antibodies (PerkinElmer, OPAL POLYMER HRP MS+RB) were used at an original dilution.

Multiplex IHC imaging and inForm analysis
Slides were imaged using a Vectra microscope.Whole slide scans were performed using the × 10 objective lens.ROIs were selected with fixed-size stamps in Phenochart (PerkinElmer), based on the previously acquired whole slide scan images.A 1 × 1 stamp (669 × 500 µm; × 20 objective lens) was employed for the Margin, while a 2 × 2 stamp (1338 × 1000 µm) was utilized for the Core, Edge, and Normal regions.To maximize viable regions in each specimen, selections were made with minimal overlap.Acquired images underwent analysis with inForm for tissue-component segmentation, distinguishing between tumor-cell (CK+) and stroma (CK-) regions, as well as cell phenotyping.The cell density within each ROI was computed by aggregating cell counts from all images and normalizing them by the total area (cells/mm^2).Multiplex IHC images were independently analyzed and blinded by three observers.

Statistical analysis
R (version 4.2.1) was employed for all statistical analyses and graphical creations.Volcano plots were generated using the "ggplot2" package, while violin plots were created using the "ggpubr" package.The Mann-Whitney test was utilized for differential gene expression analysis, tumor mutation burden analysis, single-sample gene set enrichment analysis (ssGSEA) score analysis, immunological checkpoint analysis, and HLA analysis.Correlation tests were employed for cancer stem cell infiltration and drug sensitivity tests.Additionally, the log-rank test and Kaplan-Meier analysis were utilized to compare overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) between groups.Unpaired two-sided t-tests were performed using Prism 9 (Graphpad) to analyze differences between groups.P values < 0.05 were considered statistically significant.

Informed consent statement
We confirm that informed consent was obtained from all participants involved in this study, including the use of tissue samples.Prior to their participation, all subjects or their legal guardians were provided with detailed information regarding the nature and purpose of the study, potential risks and benefits, confidentiality measures, and their rights as participants.They were given the opportunity to ask questions and clarify any concerns before providing their consent.Written consent forms were signed by all participants or their legal guardians before any data collection or procedures were carried out.This study was conducted following the ethical principles outlined in the Declaration of Helsinki and approved by the Renmin Hospital of Wuhan University Ethics Committee.

TLSs is associated with better survival prognosis in NSCLC patients
A total of 914 samples in the TCGA cohort were observed.There are 723 samples showing TLSs belonged to the TLS+ group, and the remaining 191 samples without lymphocytic infiltration to the TLS-group.As well, among the 53 samples in the study cohort, 16 cases belonged to the TLS+ group and 37 cases to the TLS-group (Fig. 1).Results of survival curves showed that the TLS+ group was associated with better OS (p = 0.022), PFS (p = 0.042), and DSS (p = 0.004) in the TCGA cohort (Fig. 2a-c).In our Study cohort, the TLS+ group also showed a better PFS (p = 0.012) than TLS-group (Fig. 2d).This further validates the prognostic effects of TLSs in patients with NSCLC.

Immune features analysis
We further explored the relationship between TLSs and the immune status of NSCLC patients in the TCGA cohort.We found that the TLS+ group had a significantly better immune status and higher levels of immune cells than the TLS-group, especially activated B cells, immature B cells, activated CD8+ T cells, and Th1 (Fig. 4e).
Next, we compared the immune cell scores of the two groups in the TCGA cohort.The results showed that the infiltration scores of most immune cells in the TLS+ group were higher than in TLS-groups, such as aDCs, B cells, check-point, CD8+ T cells, and T-helper cells (Fig. 4g).Considering the importance of checkpoint inhibitors in clinical treatment, we further analyzed the differences in ICIs expression and found substantial differences in CD27, CD28, PDCD1, TIGIT, CTLA4 and BTLA between the two groups (Fig. 4f).Next, we evaluated the potential therapeutic effectiveness of immunotherapy in both patient groups using TIDE.A higher TIDE  www.nature.com/scientificreports/prediction score, the higher the likelihood of immune evasion, suggesting that patients are less likely to benefit from ICI therapy.We found that patients in the TLS+ group had a lower TIDE score than those in the TLS-group, suggesting that the TLS+ patients might respond better to ICI therapy in NSCLC (Fig. 4a).In addition, we also discovered that cancer-associated fibroblast (CAF) and Myeloid-derived suppressor cells (MDSC) were more excellent in the TLS-group (Fig. 4b,c).Meanwhile, patients in the TLS+ group had a significantly higher immune score in the TME (Fig. 4d).

Gene set enrichment analyses
According to KEGG, pathways enriched in the TLS+ group included the Autoimmune thyroid disease, Haematopoietic cell lines, The intestinal immune network promotes IgA production, primary immunodeficiency, and systemic lupus erythematosus, whereas pathways enriched in the TLS-group included ascorbate and aldehyde metabolism, cell cycle, drug metabolism other enzymes, neuroactive ligand-receptor interactions and pathways in cancer (Fig. 5a,b).Additionally, GO revealed that genes in the TLS+ group were engaged in B-cell receptor signaling pathways, Immunoglobulin complex, Immune globulin complexes in circulation, Antigen binding, and Immunoglobulin receptor binding, whereas genes in the TLS-group were enriched in behavior, embryonic organ development, regionalization, sensory organ development, signal release (Fig. 5c,d).

Antigen presentation analysis
There was a substantial variation in HLA expression associated with antigen presentation between the TLS+ and TLS-groups.The expression of numerous HLA classes I and II was more significant in the TLS+ group than in the TLS-group in the total TCGA cohort (Fig. 5e).

Drug sensitivity analysis
We followed the impact of TLS on the efficacy of immunotherapy by TCIA.The results demonstrated that the TLS+ group was more likely to respond to CTLA4-positive/PD1 markerpositive treatment than the TLS-group www.nature.com/scientificreports/(Fig. 6a-d).This shows that patients in the TLS+ group may respond better to CTLA4-positive/PD1-positive immunotherapy, leading to a better clinical outcome.

Effect of immunotherapy is associated with the size and count of TLSs in NSCLC patients
To further explore the correlation between TLSs and the efficacy of immunotherapy in NSCLC, we divided the 53 cases in the study cohort into an effective and an ineffective group.Patients with a response evaluation of complete response (PR), partial response (CR), and stable disease (SD) for at least 6 months were in the effective group (n = 25).Those cases that didn't meet this condition in the sample were in the ineffective group (n = 28).The TLSs in the above cases were quantified by counting and measuring the area of the TLSs under whole slices.The size of the TLSs was expressed by the relative area (total measured area of TLSs/area of sectioned tissue) to reduce the size error of different sections of tissue.The results showed statistically significant differences in TLS count and TLS size between samples in the Effective and Ineffective groups (p < 0.0001 and p < 0.0001, respectively; Fig. 7a,b).This indicated that the number and size of TLSs within the whole TME may contribute to the response of immunotherapy in NSCLC.

Relationship between different T cell phenotypes and TLSs in TME CD4+ T cell phenotypes
We performed a multiplex mIHC staining on tissue sections from 18 samples in the study cohort, and simultaneous detection of CD4/CD8+ lymphocytes, CK+ tumor cells, PD1+ cells, Foxp3+ cells, and TCF1+ cells in the TME by Inform 2.6.Cell phenotyping data were obtained based on the pattern of marker expression in panel 2 (Fig. 8).Our results showed that CD4+ T cell levels were significantly higher in the TLS+ group than in the TLS-group (Fig. 9a, p = 0.013).It also showed a significantly higher level of the marker TCF1 in the TLS+ group than in the TLS-group.However, the expression of markers PD1 and Foxp3 did not show differences in the two groups (Extended Fig. 2a-c).We then co-localized other markers with CD4+ T cells and found that levels including CD4+PD1-, CD4+Foxp3-, and CD4+TCF1+ T cells were significantly higher in the TLSs+ group than in the TLS-group (Fig. 9b-d), which was consistent with the results in CD4+Foxp3+ T cells, while CD4+PD1+ T cells were not significantly different between the two groups (Extended Fig. 2d,e).Besides, higher levels of , providing an orderly and efficient site for T cell proliferation and differentiation 5,6 .In studies of NSCLC 22,23 , colorectal 24,25 , gastric 26 , and ovarian cancers 27 , researchers have found that high levels of B cells or DCs within TLSs are associated with better OS through analysis of the cellular composition of TLSs.In addition, other components within TLSs have been found to have positive prognostic value, including HEVs 28 , Chemokine-12 29 and CD3+ T cells 22 .However, it is unclear how TLSs regulate the immune response and how immune cells (particularly B cells and T cells) interact with each other 30 .Therefore, it is necessary to study the integrated role of TLSs in TME.
In our study, we included 53 NSCLC patients treated with PD1 inhibition as the study cohort.Then we obtained the dataset of 914 NSCLC pathological sections archived by CDSA from the TCGA database as the TCGA cohort and collected the corresponding transcript expression data and clinical data.When selecting specimens from the TCGA database for analysis in this study, our goal was to observe the trend of TLS-associated mutations across the entire spectrum of NSCLC cancers, including early-, intermediate-, and late-stage patients, so that 723 of 914 samples belonged to the TLS+ group; In contrast, in our own study cohort specimen,   exhibiting high expression of TLSs 14 .Paradoxically, further analysis of the mutational information obtained from the DNA sequencing data through the TCGA database revealed that the TLSs-group had a higher mutation frequency compared to the TLS+ group.We then further demonstrated that the TLS+ group is more closely related to the immune system in terms of cellular infiltration, biological function, and signaling pathways.The immune infiltration score showed that TLSs are closely associated with the infiltration of immune cells, such as CD8+ T cells, Tregs, CD4+ T cells, and NK.These cells play different roles in the anti-tumor immune response.CD4+ helper T cells promote the immune response.In contrast, Foxp3, a signature molecule of regulatory T cells (Treg), determines function of Treg and leads to Immune suppression 31,32 .We analyzed the expression of immune checkpoints in the TCGA cohort and found that most immune checkpoints were higher expressed in the TLS+ group.This further validates the predictive role of TLSs on the efficacy of immunotherapy.More importantly, we found that the count and size of TLSs in our study cohort correlated closely with the efficacy of immunotherapy in NSCLC.As a study also found in lung cancer, high-scoring TLSs showed an improved response to immunotherapy 33 .
We analyzed the immune cell components of the TME in the study cohort by mIHC and found that the expression of CD4+Foxp3+ Treg and CD4+Foxp3-helper T cells were higher in the TLS+ group than in the TLSgroup.The interaction of these T cells maintains the stability of the TME, leading to a more orderly anti-tumor immune response.Therefore, the functional status of immune cells in the TME is very important.In response to prolonged high antigen load, some CD8+ effector T cells have a reduced capacity to secrete cytokines while expressing a large number of inhibitory receptors, such as PD1, T cell immunoglobulin and mucin-domain containing-3 (Tim-3), Lymphocyte activation gene 3 (LAG-3) and CTLA4.This condition is known as "T-cell failure" 34,35 .Our results show that TLSs are associated with high levels of CD8+PD1-T cells and CD4+PD1-T cells.In addition, CD8+TCF1+ T cells were also significantly highly expressed in the TLS+ group.TCF1, a T cell-specific transcription factor, is essential for early T cell development and is a downstream effector molecule of the classical Wnt signaling Pathway 36 .TCF1 was found to be essential for the self-replication of CD8+ memory T cells in the tumour microenvironment and to promote response to immune checkpoint inhibitors in tumour patients 37,38 .The low expression of inhibitory receptors in the immune microenvironment and the high expression of TCF1 may represent a T cell state that is the opposite of "T cell failure" 39 .Furthermore, our results showed that CD8+PD1-TCF1+ T cells were significantly higher expressed in the TLS+ group, and it is more beneficial to the survival prognosis and immunotherapeutic efficacy of NSCLC patients in our previous study.This also suggested that TME with TLSs indicates a more active immune state.Consistently, a previous study of sarcoma also suggests that the high immune group (E group) showed improved survival and response to PD1 blockade therapy 8 .
Naturally, there are some limitations to our study.Primarily, the pathologic specimens in both the TCGA database and our own study cohort were large specimens of single pathology sections, which provide a more comprehensive picture of the patient's immune microenvironment and TLS than limited microarray specimens.However, due to the difficulty of obtaining specimens as well as the difficulty and cost of the test manipulation, there have been fewer studies that have chosen large samples for exploration.In order to obtain more accurate data, we chose a pathologic large sample.The source of our specimens was patients with advanced NSCLC who received immunotherapy.Previous studies on TLS did not mention the treatment of the patients and few immunotherapy patients were seen, only 2 cohort studies on sarcoma and renal cancer were published simultaneously in Nature 2020, involving immunotherapy, and similar studies on NSCLC were not seen; most importantly, we further explored the size of TLS in the TME of these advanced NSCLC patients by mIHC and number in relation to immunotherapy efficacy, and also explored the expression of TLS and T-cell surface molecules.This also further increased the difficulty of specimen collection.Furthermore, when we initially selected specimens from the TCGA database for the original letter analysis, we wished to study the trend of TLS-associated mutations across the entire spectrum of NSCLC cancers, and therefore did not differentiate between patients' tumor stages.Instead, the specimens in our study cohort were intended to further explore the efficacy of immunotherapy, and therefore the specimens that could be obtained were from patients with advanced NSCLC.It is also due to the scarcity of pathological specimens and the difficulty of obtaining tissues from this group of patients that led to the small number of qualified samples we were able to collect, which is a shortcoming of this experiment, and we will continue to collect and expand the sample size in the clinic at a later stage.
In summary, we found that TLSs were a strong predictor of survival and immunotherapy efficacy in NSCLC patients, both the count and size of TLS correlate with the efficacy of immunotherapy.In addition, TLSs were closely associated with both CD4+ T cells and CD8+ T cells in the tumor microenvironment.T cells closely associated with TLS+, such as CD4+Foxp3+ regulatory T cells, and CD4+Foxp3-helper T cells maintain homeostasis of the immune microenvironment and contribute to the anti-tumor immune response of CD8+ effector T cells.Moreover, TLS+ is also associated with higher levels of PD1-TCF1+ stem cell-like effector T cells, which act in concert to activate a sufficiently powerful anti-cancer killing force.

Figure 3 .
Figure 3. Tumor mutation burden analysis.Assessment of the differences in the mutational landscape between the TLS+ (a) and TLS-(b) groups.

Figure 7 .
Figure 7.The number and size of TLSs correlate with response to immunotherapy in NSCLC patients.This result was performed in the Study cohorts.Comparison of TLS count and immunotherapy efficacy (a).Comparison of TLSs size and immunotherapy efficacy (b).two-tailed unpaired t-test for two group comparisons.Significance markers, ns: p > 0.05;*P < 0.05, **P < 0.01, ***P < 0.001.